Development of a Product Recommendation Method for the Real-Time Cart Context in Supermarkets by Integrating Cooking Recipes and Purchase Co-occurrences

The effectiveness of product recommendation systems is critical to enhancing customer experiences and boosting sales in the rapidly evolving retail domain, especially in supermarkets. Thus, in this study, we design an innovative recommendation approach for physical supermarkets, and our approach integrates insights from previous purchasing patterns with current shopping cart compositions augmented with recipe-based information. As this approach deviates from traditional strategies, which primarily rely on historical data, it dynamically addresses shoppers’ immediate preferences and recommends products that suit their intended purchases. Furthermore, we evaluate the effectiveness of this technique using data from smart shopping carts in a brick-and-mortar supermarket, revealing significant improvements in key performance indicators, such as Recall, Precision, and the F1 score, than with the existing methods. These results highlight the benefits of integrating real-time cart data with historical purchasing patterns, offering a path to more personalized and efficient recommendations in retail environments. This study illustrates the potential of such integrated approaches toward significantly improving in-store shopping experiences.